Knowledge Base Server (Qdrant)

Read-only MCP server with hybrid search — dense semantic (all-MiniLM-L6-v2) + sparse keyword (BM-25) — fused with Qdrant's built-in RRF.

Stack

Layer	Tool	Cost
MCP framework	FastMCP	Free
Vector DB	Qdrant Cloud	Free (1 GB)
Embeddings	FastEmbed (local, ONNX)	Free
Hosting	Render	Free tier
ChatGPT	Deep Research connector	Free (Pro plan)

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Setup

1. Install dependencies

python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt

2. Set up Qdrant Cloud

1. Go to https://cloud.qdrant.io → create a free cluster
2. Copy the cluster URL and API key from the Access tab
3. The collection is created automatically on first ingest

3. Configure .env

cp .env.example .env
# fill in QDRANT_URL, QDRANT_API_KEY, MCP_API_KEY

4. Upload documents

python ingest.py --file report.pdf --dept 1 --pos 2
python ingest.py --file notes.md
python ingest.py --list
python ingest.py --delete <document_id>

Supported formats: pdf, docx, md, txt, rst

5. Test locally

python server.py
# → http://localhost:8000/mcp

6. Deploy to Render

1. Push to GitHub (.env is gitignored)
2. Render → New Web Service → Connect GitHub repo
3. Add env vars: QDRANT_URL, QDRANT_API_KEY, KEYCLOAK_REALM_URL, KEYCLOAK_CLIENT_ID
4. Copy your public URL

7. Connect to ChatGPT

• Settings → Connectors → Add → paste Render URL + /mcp/
• Auth: Bearer token → your MCP_API_KEY
• Use via + → Deep Research → select your connector

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Tools (all read-only)

Tool	Description
search(query, top_k)	Hybrid search. Returns point IDs.
fetch(id)	Get chunk content by point ID.
list_documents()	List all documents.
get_document(id)	Full text of a document by document_id.

search + fetch follow the ChatGPT Deep Research contract.